A Written History on Visual Data

It’s hard to believe that what I’m learning right now originated as a way to chart the stars and discover new lands. Maybe that’s why every time I look at a pie chart I can’t help but giggle. The evolution of data visualization is incredible, its purpose today is almost completely different from its original purpose. Data visualization has had a long and constantly changing history since its inception and to follow its evolution is fascinating.

The history unofficially begins in 1644 when Michael Florent van Langren was believed to create the first visual representation of statistical data. I consider van Langren to be the grand daddy of data visualization. After van Langren the next big advancements in data visualization came in 1669 with the first graph of a continuous distribution function and in 1680 with the first weather map.

The start of visual thinking as we know it today began in the 18th century. This century brought us who I consider to be the daddy of visual thinking, William Playfair. Playfair is the inventor of most of the graphical forms widely used today, the line graph, bar chart, pie chart and circle graph. This data viz creation momentum continued into the 19th century which had explosive growth in statistical graphic and thematic mapping. That being said data viz at this time was still relatively unpopular as a whole.

Unfortunately advancements came to a screeching halt in 1900 and data visualization entered into its dark ages. No advancements were made and people wouldn’t care about data visualization again until it emerged out of the darkness in 1975 as a result of the creation of the PC. This is due to the convenience the PC provided, simply imputing your data in a PC could create a very nice looking chart or graph for any topic. According to Scott Berinato in his book, Good Charts,

” By the mid-century the US government had become a complex and data-driven enterprise that demanded abstraction in unprecedented volume.”

Since 1975 data viz has blossomed into a mature multi disciplinary research area and taken the recognizable form we see it as today. Today data visualization is seen in almost every discipline and has become an integral part of our everyday lives. No longer is data viz reserved solely for charting the stars or record the weather, now it is a second language that many are desperate to become fluent in.

The current state of data viz brings both positives and negatives. On one hand, anyone can do data viz, on the other hand… anyone can do data viz. What I mean by this is that data viz is more accessible and easy to use than ever before. This accessibility is incredible, it can open more possibilities in the industry, people can find a new skill they’re passionate about and the opportunity for growth is huge. That being said this accessibility leaves a lot of room for error, a lot of bad charts and graphs will be created and misinformation about data viz will have an easier time to spread.

And I’m sad to say that unfortunately it is very easy to make bad data viz decisions. There are some simple distinctions that separate good charts from bad ones in my opinion. A good chart follows the rule of Edwards Tufte, “above all else show the data” this is simple, a chart chart at its core must convey data. In addition to that a good chart must be eye catching, because there is an over saturation with data viz in today’s world, charts must keep having a pleasant visual in mind. The last component of what makes a good chart is keeping the ideas of Jacques Bertin in mind, specifically, “Use the best method available for showing your data”. This basically means that even if you’ve decided that a certain chart is the best way to display your data but you know that your client responds better to a different type of chart, you must keep your client in mind and adapt what you think you know.

It’s different and funky and will definitely get me to pay attention. Along with that I need a good chart to be readable. I don’t want to spend the time deciphering a chart, I need it to be easy to understand for it to be good. An example of this is below…

We’ve come a long way from the star charts of yesterday. Data viz has had a fascinating and rich evolution and I for one cannot wait to see what the future holds. However we must take what we have learned from the past and combine it with the needs of today if we want any real progress to be made.